With wide applications of computers and automated data collection tools , rnassive amounts of data have been continuously collected and stored in databases , which creates an imminent need and great opportunities for mining interesting knowledge from data association rule mining is one kind of data mining techniques which discovers strong association or correlation relationships among data the discovered rules may help market basket or cross - sales analysis , decision making , and business management 随着计算机和自动数据收集工具的应用，大量的数据已经被持续地收集和存储在数据库中，由此产生了从大量数据库中挖掘令人关注的信息的巨大需求。关联规则是一种从大量数据库中发现强关联或内在联系的数据挖掘技术，发现的规则可帮助进行市场或销售分析，决策支持和企业管理。
In this paper , we introduce an enterprise selling management system based on oracle , it includes seven parts : distributary business , authorized business , selling plan , basic data , stock , producing notice , selling analysis . this system relates to several departments including production , selling , stock and finance 本文所述的是基于oracle的企业销售管理系统，它包括了经销业务、委托代销业务、销售计划、基础数据、库存、生产通知、销售分析七个部分，该系统涉及了生产、销售、库存、财务等几个部门的业务。
It ' s a pity that although there are many papers and articles focused on data mining published every year , most of them deal with data mining concept and abstract algorithm theory , it is hardly to see their real implementation and application , in this context , when i was in my graduate exercitation in a company in beijing , which focus on developing supermarket software , i joined and completed an olap ( online analytical processing ) project , merchandise analysis and sale report system , which based on microsoft analysis service and microsoft sql server . i also design and implement three important algorithms : merchandise association rule algorithm based on multi - level merchandise category , supermarket member customer shopping frequent sequence generating algorithm , customer classification ( decision tree ) algorithm which based on information entropy and conditional probability tree , and they all achieve expected result 本文作者在实习期间，参与并完成了基于微软分析服务器的销售分析与报表系统；并在公司即将开始的数据挖掘项目中，完成了多个重要算法的设计和c + +程序实现：基于多层分类商品树的商品关联规则算法，会员顾客的购物频繁序列模式产生算法；基于信息熵理论和条件概率树的会员顾客分类(决策树)算法，并分别使用数据进行了测试，取得了较好的结果。
